Our method also generalizes to the image domain as shown in Table REF . CLASTER outperforms previous work in five out of eight tasks and obtains comparable results in the remaining three tasks. We compare our approach to several state-of-the-art methods: Region Graph Embedding Network with the Parts Relation Reasoning branch (RGEN) and without the branch (R-PRR) [1]}, the evaluation of output embeddings(SJE) [2]}, the feature generating networks (WGAN) [3]}, the feature generating framework (VAEGAN) [4]} and the latent embedding feedback method (LEF) [5]}. [1]}).
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